1. Field of the Invention
The present invention relates to controllers for electronic control systems, and particularly to a robust controller for nonlinear MIMO (multiple-input, multiple output) systems.
2. Description of the Related Art
Nonlinear control has been a subject of immense interest among researchers. Almost every real life process has nonlinear behavior. At the same time, systems in real life are prone to change in parameters due to several internal and external factors affecting the process. Hence, robust controller design for nonlinear systems has become a vital requirement in modern control theory and practice. The nonlinearity in systems is most often beyond the limit of linear controllers. This has caused many nonlinear control techniques to surface over the past two decades, such as nonlinear PID (proportional-integral-derivative) control.
In particular, feedback linearization techniques have drawn much interest recently. The basic idea of feedback linearization is to transform the nonlinear system into a linear system so that linear control techniques can be applied easily. However, this requires exact knowledge of plant nonlinearities, thereby making the performance of feedback linearization limited, as well as conditional. Adaptive control techniques surfaced in order to reduce the dependency on an exact model. However, several adaptive control techniques require a reference model or system identification of the plant as the plant is running. Another notable nonlinear control technique known as sliding mode control (SMC) stems from an extension of PID control. SMC alters the dynamics of a nonlinear system by applying high-frequency switching control. It switches from one continuous structure to another based on the current position in the state-space. However, due to the hard sliding mode control action, SMC has to be applied with more care than other nonlinear control techniques in order to avoid energy loss and damage to plants.
On the other hand, among several techniques that have surfaced in the recent past to eater to the challenging problem of nonlinear control, artificial neural networks (ANN) have emerged as an efficient class of machines capable of learning complex nonlinear functions. The use of neural networks in controller design is therefore a natural choice. Neural networks have been used for pattern recognition, function approximation, time-series prediction, and classification problems for quite some time. The ability of neural networks to map complex input-output relationships make them ideal for compensating plant nonlinearities, and hence make them ideal for controller design problem.
Several techniques involving neural networks have surfaced in the past decade for nonlinear adaptive control. However, in many adaptive control approaches, it is well understood that there exists a necessary assumption that the controlled system has to be a minimum-phase system, i.e., the zero dynamics of the system must be stable. Several nonlinear adaptive control techniques known in the art have based their controller on this assumption. Many other nonlinear controllers known in the art require state-feedback. While state-feedback poses no harm to controller performance, it requires state measurement at every sampling time using sensors, which can increase the implementation cost if the system has a significant number of states. As compared to state-feedback, an output-feedback scheme can be less expensive, due to the fact that controlled outputs are a smaller subset of system states, in most cases. Such controllers also require a process model derived from mass and energy balance equations, thus requiring a rigorous model. An adaptive neural network control scheme for systems containing non-smooth nonlinearities in the actuator device has also been proposed. Such a control scheme is limited to single-input single-output (SISO) systems.
Thus, a robust controller for nonlinear MIMO systems solving the aforementioned problems is desired.